TAE提升夜间无人机追踪，专注目标区域增强
TAE: Target-aware enhancer for nighttime UAV tracking
- 基于跟踪框弱监督，只对目标区域进行增强
- 多曲线自适应融合，有效抑制背景噪声
- 适配夜间追踪场景，适合低光环境研究者
夜间低光照条件下图像严重退化，是制约无人机单目标跟踪全天应用的核心瓶颈。现有图像增强方法难以区分目标与背景，易放大背景噪声或损伤目标特征。为此，我们提出TAE——一种面向夜间目标跟踪的靶向增强框架。该框架利用跟踪边界框提供的弱监督信号，实现区域感知增强，确保操作聚焦于目标区域。同时采用自适应RGB多曲线融合机制，实现不同区域的精细化建模与动态调节。为推动该领域研究，我们还构建了新的基准DarkSOT，包含268个序列、9类目标。在DarkSOT和UAVDark135上的实验表明，TAE显著提升了低光夜间场景下的跟踪性能，具备强鲁棒性与泛化能力。DarkSOT数据集已开源：https://github.com/Fu0511/DarkSOT-Dataset。
原文摘要 · Abstract (English)
Severe image degradation under low-light nighttime conditions constitutes a core bottleneck preventing all-day applications for UAV-based single object tracking. Existing image enhancement methods often struggle to distinguish between target and background regions, which can easily lead to amplified background noise or compromise target features. To overcome this limitation, we propose TAE, a target-aware low-light enhancement framework tailored for nighttime object tracking. Guided explicitly by weak supervisory signals from tracking bounding boxes, the framework performs region-aware enhancement to ensure operations focus on the target area. It further adopts an adaptive RGB multi-curve fusion mechanism to achieve refined modeling and adaptive adjustment across different regions. To facilitate research in this domain, we also contribute DarkSOT, a new benchmark for nighttime UAV tracking, comprising 268 sequences across 9 target categories. Experimental results on the DarkSOT and UAVDark135 demonstrate that TAE significantly improves tracking performance in low-light nighttime scenarios, exhibiting strong robustness and generalization. The DarkSOT dataset is available at https://github.com/Fu0511/DarkSOT-Dataset.
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